Field
The disclosed embodiments relate to anomaly detection. More specifically, the disclosed embodiments relate to techniques for performing statistical detection of site speed performance anomalies.
Related Art
Web performance is important to the operation and success of many organizations. In particular, a company with an international presence may provide websites, web applications, mobile applications, databases, content, and/or other services or resources through multiple data centers around the globe. An anomaly or failure in a server or data center may disrupt access to the service or resources, potentially resulting in lost business for the company and/or a reduction in consumer confidence that results in a loss of future business. For example, high latency in loading web pages from the company's website may negatively impact the user experience with the website and deter some users from returning to the website.
The distributed nature of web-based resources may complicate the accurate detection and analysis of web performance anomalies and failures. For example, a performance of a website may be monitored by setting a threshold for a performance metric such as page load time and generating an alert of an anomaly when the performance metric exceeds the threshold. Because the website may be accessed through multiple data centers with different amounts of computational and/or network bandwidth, the threshold may be manually selected to accommodate the site speeds supported by each data center location.
At the same time, a custom threshold for each data center may be unable to account for fluctuations in network traffic. For example, the page load time in a data center may increase during periods of high network traffic and decrease during periods of low network traffic. However, a threshold that does not account for peaks and troughs in network traffic may generate many false alarms during the peaks and detect only large anomalies during the troughs.
Consequently, detection and analysis of anomalies in web performance may be improved by dynamically adapting the monitoring of performance metrics to conditions that affect the performance metrics.
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